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How to use a_max method in pandera
Best Python code snippet using pandera_python
shermann.py
Source:shermann.py 
1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4import time5method = 'HybridUCB'6class HybridUCB:7 def __init__(self):8 self.article_features = {}9 # upper bound coefficient10 self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)11 r1 = 20.12 r0 = -0.513 self.r = (r0, r1)14 # dimension of user features = d15 self.d = 516 # dimension of article features = k17 self.k = self.d*self.d18 # A0 : matrix to compute hybrid part, k*k19 self.A0 = np.identity(self.k)20 self.A0I = np.identity(self.k)21 # b0 : vector to compute hybrid part, k22 self.b0 = np.zeros((self.k, 1))23 # Aa : collection of matrix to compute disjoint part for each article a, d*d24 self.Aa = {}25 # AaI : collection of matrix to compute disjoint part for each article a, d*d26 self.AaI = {}27 # Ba : collection of matrix to compute hybrid part, d*k28 self.Ba = {}29 # BaT : collection of matrix to compute hybrid part, d*k30 self.BaT = {}31 # ba : collection of vectors to compute disjoin part, d*132 self.ba = {}33 self.updateCycle = 1034 self.lastUp = 035 # other dicts to speed up computation36 self.AaIba = {}37 self.AaIBa = {}38 self.BaTAaI = {}39 self.theta = {}40 #additional stuff that is computated in the update function instead of recommend41 self.A0IBaTAaI = {}42 self.A0IBaA0IBaTAaI = {}43 self.lastart = []44 self.beta = np.zeros((self.k, 1))45 self.index = {}46 self.a_max = 047 self.z = None48 self.zT = None49 self.xaT = None50 self.xa = None51 # Evaluator will call this function and pass the article features.52 # Check evaluator.py description for details.53 def set_articles(self, art):54 # init collection of matrix/vector Aa, Ba, ba55 i = 056 art_len = len(art)57 self.art = art58 self.article_features = np.zeros((art_len, 1, self.d))59 self.Aa = np.zeros((art_len, self.d, self.d))60 self.AaI = np.zeros((art_len, self.d, self.d))61 self.Ba = np.zeros((art_len, self.d, self.k))62 self.BaT = np.zeros((art_len, self.k, self.d))63 self.ba = np.zeros((art_len, self.d, 1))64 self.AaIba = np.zeros((art_len, self.d, 1))65 self.AaIBa = np.zeros((art_len, self.d, self.k))66 self.BaTAaI = np.zeros((art_len, self.k, self.d))67 self.A0IBaTAaI = np.zeros((art_len, self.k, self.d))68 self.A0IBaA0IBaTAaI = np.zeros((art_len, self.d, self.d))69 self.theta = np.zeros((art_len, self.d, 1))70 for key in art:71 self.index[key] = i72 self.article_features[i] = art[key]73 self.Aa[i] = np.identity(self.d)74 self.AaI[i] = np.identity(self.d)75 self.Ba[i] = np.zeros((self.d, self.k))76 self.BaT[i] = np.zeros((self.k, self.d))77 self.ba[i] = np.zeros((self.d, 1))78 self.AaIba[i] = np.zeros((self.d, 1))79 self.AaIBa[i] = np.zeros((self.d, self.k))80 self.BaTAaI[i] = np.zeros((self.k, self.d))81 self.A0IBaTAaI[i] = np.zeros((self.k, self.d))82 self.A0IBaA0IBaTAaI[i] = np.zeros((self.d, self.d))83 self.theta[i] = np.zeros((self.d, 1))84 i += 185 # This function will be called by the evaluator.86 # Check task description for details.87 def update(self, reward):88 global ttime89 #print reward90 if reward == -1:91 pass92 else:93 r = self.r[reward]94 self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])95 self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])96 self.Aa[self.a_max] += np.dot(self.xa, self.xaT)97 B = np.divide(98 np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),99 (1 + np.dot(np.dot(self.xaT, self.AaI[self.a_max]), self.xa)))100 self.AaI[self.a_max] = self.AaI[self.a_max] - B101 self.Ba[self.a_max] += np.dot(self.xa, self.zT)102 self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])103 self.ba[self.a_max] += r * self.xa104 self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])105 self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])106 self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])107 self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])108 self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))109 #change to LSG?110 self.A0I = linalg.inv(self.A0)111 self.beta = np.dot(self.A0I, self.b0)112 #self.beta = linalg.solve(self.A0, self.b0)113 # if not np.array_equal(beta1, self.beta.all):114 # print115 self.A0IBaTAaI[self.a_max] = np.dot(self.A0I,self.BaTAaI[self.a_max])116 self.A0IBaA0IBaTAaI[self.a_max] = np.dot(self.AaIBa[self.a_max],self.A0IBaTAaI[self.a_max])117 #do this here !!!!!118 #self.lastUp = self.lastUp + 1119 #if self.lastUp > self.updateCycle:120 #self.matrixUpdate()121 #self.lastUp = 0122 123 self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))124 self.lastart = []125 def matrixUpdate(self):126 #global ttime127 #ttime = ttime - time.time()128 for ind in range(self.BaTAaI.shape[0]):129 #print self.A0IBaTAaI130 self.A0IBaTAaI[ind] = np.dot(self.A0I,self.BaTAaI[ind])131 self.A0IBaA0IBaTAaI[ind] = np.dot(self.AaIBa[ind],self.A0IBaTAaI[ind])132 #ttime = ttime + time.time()133 # This function will be called by the evaluator.134 # Check task description for details.135 # Use vectorized code to increase speed136 def recommend(self, timestamp, user_features, articles):137 global ttime138 article_len = len(articles)139 # za : feature of current user/article combination, k*1140 self.xaT = np.array([user_features])141 self.xa = np.transpose(self.xaT)142 # recommend using hybrid ucb143 # fast vectorized for loops144 index = [self.index[article] for article in articles]145 ttime = ttime - time.time()146 if cmp(self.lastart,index)!=0:147 self.article_features_tmp = self.article_features[index]148 #preprocess matrices149 self.preA0IBaA0IBaTAaI=self.A0IBaA0IBaTAaI[index]150 self.preAaI = self.AaI[index]151 self.preA0IBaTAaI = self.A0IBaTAaI[index]152 self.preTheta = self.theta[index]153 self.lastart = index154 155 ttime = ttime + time.time()156 zaT_tmp = np.einsum('i,j', self.article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)157 #print zaT_tmp.shape158 za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))159 A0IBaTAaIxa_tmp = np.dot(self.preA0IBaTAaI, self.xa)160 #looks good :)161 #print A0IBaTAaIxa_tmp1-A0IBaTAaIxa_tmp162 A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)163 A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp164 sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)165 #ttime = ttime - time.time()166 AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.preAaI, self.xa) + np.dot(self.preA0IBaA0IBaTAaI,self.xa)167 #ttime = ttime + time.time()168 #print AaIxa_add_AaIBaA0IBaTAaIxa_tmp - AaIxa_add_AaIBaA0IBaTAaIxa_tmp1169 sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))170 sa_tmp = sa_1_tmp + sa_2_tmp171 xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.preTheta,(0,2,1)),self.xa),(0,2,1))172 max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))173 self.z = za_tmp[max_index]174 self.zT = zaT_tmp[max_index]175 art_max = index[max_index]176 # article index with largest UCB177 # global a_max, entries178 self.a_max = art_max179 # return np.random.choice(articles)180 return articles[max_index]181algorithms = {182 'HybridUCB': HybridUCB()183}184algorithm = algorithms[method]185set_articles = algorithm.set_articles186update = algorithm.update187ttime = 0...
example.py
Source:example.py
1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4method = 'HybridUCB'5combcut = 06artcut = 07usercut = 08class HybridUCB:9 def __init__(self):10 self.article_features = {}11 # upper bound coefficient12 self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)13 r1 = 20.14 r0 = -0.515 self.r = (r0, r1)16 # dimension of user features = d17 self.d = 618 # dimension of article features = k19 self.k = self.d*self.d-combcut20 print self.k21 #select subset of features22 ind = np.arange(self.d*self.d)23 np.random.shuffle(ind)24 self.scols = ind[:self.k]25 print self.scols26 # A0 : matrix to compute hybrid part, k*k27 self.A0 = np.identity(self.k)28 self.A0I = np.identity(self.k)29 # b0 : vector to compute hybrid part, k30 self.b0 = np.zeros((self.k, 1))31 # Aa : collection of matrix to compute disjoint part for each article a, d*d32 self.Aa = {}33 # AaI : collection of matrix to compute disjoint part for each article a, d*d34 self.AaI = {}35 # Ba : collection of matrix to compute hybrid part, d*k36 self.Ba = {}37 # BaT : collection of matrix to compute hybrid part, d*k38 self.BaT = {}39 # ba : collection of vectors to compute disjoin part, d*140 self.ba = {}41 # other dicts to speed up computation42 self.AaIba = {}43 self.AaIBa = {}44 self.BaTAaI = {}45 self.theta = {}46 self.beta = np.zeros((self.k, 1))47 self.index = {}48 self.a_max = 049 self.z = None50 self.zT = None51 self.xaT = None52 self.xa = None53 # Evaluator will call this function and pass the article features.54 # Check evaluator.py description for details.55 def set_articles(self, art):56 # init collection of matrix/vector Aa, Ba, ba57 i = 058 art_len = len(art)59 self.article_features = np.zeros((art_len, 1, self.d))60 self.Aa = np.zeros((art_len, self.d, self.d))61 self.AaI = np.zeros((art_len, self.d, self.d))62 self.Ba = np.zeros((art_len, self.d, self.k))63 self.BaT = np.zeros((art_len, self.k, self.d))64 self.ba = np.zeros((art_len, self.d, 1))65 self.AaIba = np.zeros((art_len, self.d, 1))66 self.AaIBa = np.zeros((art_len, self.d, self.k))67 self.BaTAaI = np.zeros((art_len, self.k, self.d))68 self.theta = np.zeros((art_len, self.d, 1))69 for key in art:70 self.index[key] = i71 self.article_features[i] = art[key]72 self.Aa[i] = np.identity(self.d)73 self.AaI[i] = np.identity(self.d)74 self.Ba[i] = np.zeros((self.d, self.k))75 self.BaT[i] = np.zeros((self.k, self.d))76 self.ba[i] = np.zeros((self.d, 1))77 self.AaIba[i] = np.zeros((self.d, 1))78 self.AaIBa[i] = np.zeros((self.d, self.k))79 self.BaTAaI[i] = np.zeros((self.k, self.d))80 self.theta[i] = np.zeros((self.d, 1))81 i += 182 # This function will be called by the evaluator.83 # Check task description for details.84 def update(self, reward):85 #print reward86 if reward == -1:87 pass88 else:89 r = self.r[reward]90 self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])91 self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])92 self.Aa[self.a_max] += np.dot(self.xa, self.xaT)93 B = np.divide(94 np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),95 (1 + np.dot(np.dot(self.xaT,self.AaI[self.a_max]),self.xa)))96 self.AaI[self.a_max] = self.AaI[self.a_max] - B97 #A = linalg.inv(self.Aa[self.a_max])98 self.Ba[self.a_max] += np.dot(self.xa, self.zT)99 self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])100 self.ba[self.a_max] += r * self.xa101 self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])102 self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])103 self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])104 self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])105 self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))106 self.A0I = linalg.inv(self.A0)107 self.beta = np.dot(self.A0I, self.b0)108 self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))109 # This function will be called by the evaluator.110 # Check task description for details.111 # Use vectorized code to increase speed112 def recommend(self, timestamp, user_features, articles):113 article_len = len(articles)114 # za : feature of current user/article combination, k*1115 self.xaT = np.array([user_features])116 self.xa = np.transpose(self.xaT)117 # recommend using hybrid ucb118 # fast vectorized for loops119 index = [self.index[article] for article in articles]120 article_features_tmp = self.article_features[index]121 #zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)122 #use different features123 #print "test"124 #print article_features_tmp.shape125 #print self.scols126 zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.d*self.d)[:,:,self.scols]127 #print zaT_tmp.shape128 za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))129 #np.dot(self.A0I, np.dot(BaTAaI_tmp, self.xa)) (20, 36, 1)130 A0IBaTAaIxa_tmp = np.transpose(np.dot(np.transpose(np.dot(self.BaTAaI[index], self.xa), (0,2,1)), np.transpose(self.A0I)), (0,2,1))131 A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)132 A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp133 # np.dot(zaT_tmp, A0Iza_diff_2A0IBaTAaIxa_tmp), (20, 1, 1)134 sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)135 # np.dot(AaIBa_tmp, A0IBaTAaIxa_tmp)136 AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.AaI[index], self.xa) + np.sum(np.transpose(self.AaIBa[index], (0,2,1)).reshape(article_len, self.k,self.d,1)*A0IBaTAaIxa_tmp.reshape(article_len,self.k,1,1),-3)137 sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))138 sa_tmp = sa_1_tmp + sa_2_tmp139 # np.dot(self.xaT, self.theta[article])140 xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.theta[index],(0,2,1)),self.xa),(0,2,1))141 max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))142 self.z = za_tmp[max_index]143 self.zT = zaT_tmp[max_index]144 art_max = index[max_index]145 # article index with largest UCB146 # global a_max, entries147 self.a_max = art_max148 # return np.random.choice(articles)149 return articles[max_index]150algorithms = {151 'HybridUCB': HybridUCB()152}153algorithm = algorithms[method]154set_articles = algorithm.set_articles155update = algorithm.update...
shermannold.py
Source:shermannold.py
1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4method = 'HybridUCB'5class HybridUCB:6 def __init__(self):7 self.article_features = {}8 # upper bound coefficient9 self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)10 r1 = 20.11 r0 = -0.512 self.r = (r0, r1)13 # dimension of user features = d14 self.d = 615 # dimension of article features = k16 self.k = self.d*self.d17 # A0 : matrix to compute hybrid part, k*k18 self.A0 = np.identity(self.k)19 self.A0I = np.identity(self.k)20 # b0 : vector to compute hybrid part, k21 self.b0 = np.zeros((self.k, 1))22 # Aa : collection of matrix to compute disjoint part for each article a, d*d23 self.Aa = {}24 # AaI : collection of matrix to compute disjoint part for each article a, d*d25 self.AaI = {}26 # Ba : collection of matrix to compute hybrid part, d*k27 self.Ba = {}28 # BaT : collection of matrix to compute hybrid part, d*k29 self.BaT = {}30 # ba : collection of vectors to compute disjoin part, d*131 self.ba = {}32 # other dicts to speed up computation33 self.AaIba = {}34 self.AaIBa = {}35 self.BaTAaI = {}36 self.theta = {}37 self.beta = np.zeros((self.k, 1))38 self.index = {}39 self.a_max = 040 self.z = None41 self.zT = None42 self.xaT = None43 self.xa = None44 # Evaluator will call this function and pass the article features.45 # Check evaluator.py description for details.46 def set_articles(self, art):47 # init collection of matrix/vector Aa, Ba, ba48 i = 049 art_len = len(art)50 self.article_features = np.zeros((art_len, 1, self.d))51 self.Aa = np.zeros((art_len, self.d, self.d))52 self.AaI = np.zeros((art_len, self.d, self.d))53 self.Ba = np.zeros((art_len, self.d, self.k))54 self.BaT = np.zeros((art_len, self.k, self.d))55 self.ba = np.zeros((art_len, self.d, 1))56 self.AaIba = np.zeros((art_len, self.d, 1))57 self.AaIBa = np.zeros((art_len, self.d, self.k))58 self.BaTAaI = np.zeros((art_len, self.k, self.d))59 self.theta = np.zeros((art_len, self.d, 1))60 for key in art:61 self.index[key] = i62 self.article_features[i] = art[key]63 self.Aa[i] = np.identity(self.d)64 self.AaI[i] = np.identity(self.d)65 self.Ba[i] = np.zeros((self.d, self.k))66 self.BaT[i] = np.zeros((self.k, self.d))67 self.ba[i] = np.zeros((self.d, 1))68 self.AaIba[i] = np.zeros((self.d, 1))69 self.AaIBa[i] = np.zeros((self.d, self.k))70 self.BaTAaI[i] = np.zeros((self.k, self.d))71 self.theta[i] = np.zeros((self.d, 1))72 i += 173 # This function will be called by the evaluator.74 # Check task description for details.75 def update(self, reward):76 #print reward77 if reward == -1:78 pass79 else:80 r = self.r[reward]81 self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])82 self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])83 self.Aa[self.a_max] += np.dot(self.xa, self.xaT)84 B = np.divide(85 np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),86 (1 + np.dot(np.dot(self.xaT, self.AaI[self.a_max]), self.xa)))87 self.AaI[self.a_max] = self.AaI[self.a_max] - B88 self.Ba[self.a_max] += np.dot(self.xa, self.zT)89 self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])90 self.ba[self.a_max] += r * self.xa91 self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])92 self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])93 self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])94 self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])95 self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))96 #change to LSG?97 self.A0I = linalg.inv(self.A0)98 self.beta = np.dot(self.A0I, self.b0)99 #self.beta = linalg.solve(self.A0, self.b0)100 # if not np.array_equal(beta1, self.beta.all):101 # print102 self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))103 # This function will be called by the evaluator.104 # Check task description for details.105 # Use vectorized code to increase speed106 def recommend(self, timestamp, user_features, articles):107 article_len = len(articles)108 # za : feature of current user/article combination, k*1109 self.xaT = np.array([user_features])110 self.xa = np.transpose(self.xaT)111 # recommend using hybrid ucb112 # fast vectorized for loops113 index = [self.index[article] for article in articles]114 article_features_tmp = self.article_features[index]115 zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)116 za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))117 #np.dot(self.A0I, np.dot(BaTAaI_tmp, self.xa)) (20, 36, 1)118 A0IBaTAaIxa_tmp = np.transpose(np.dot(np.transpose(np.dot(self.BaTAaI[index], self.xa), (0,2,1)), np.transpose(self.A0I)), (0,2,1))119 A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)120 A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp121 # np.dot(zaT_tmp, A0Iza_diff_2A0IBaTAaIxa_tmp), (20, 1, 1)122 sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)123 # np.dot(AaIBa_tmp, A0IBaTAaIxa_tmp)124 AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.AaI[index], self.xa) + np.sum(np.transpose(self.AaIBa[index], (0,2,1)).reshape(article_len, self.k,self.d,1)*A0IBaTAaIxa_tmp.reshape(article_len,self.k,1,1),-3)125 sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))126 sa_tmp = sa_1_tmp + sa_2_tmp127 # np.dot(self.xaT, self.thea[article])128 xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.theta[index],(0,2,1)),self.xa),(0,2,1))129 max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))130 self.z = za_tmp[max_index]131 self.zT = zaT_tmp[max_index]132 art_max = index[max_index]133 # article index with largest UCB134 # global a_max, entries135 self.a_max = art_max136 # return np.random.choice(articles)137 return articles[max_index]138algorithms = {139 'HybridUCB': HybridUCB()140}141algorithm = algorithms[method]142set_articles = algorithm.set_articles143update = algorithm.update...
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